ilo_dynamic_panel

For each of the countries a time series of 34 observations on 4 
variables including the logarithm of Gross Domestic Product (gdp), as well as
the labour market outcomes including the unemployment rate (UR), employment 
rate (EPR), labour force participation rate (LFPR). The missing observations 
are filled using imputation method.
Last data update was implemented on 2025-09-26.

datasets

Provides Bayesian estimation and forecasting of dynamic panel data using
Bayesian Panel Vector Autoregressions with hierarchical prior distributions.
The models include country-specific VARs that share a global prior
distribution that extend the model by Jarociński (2010) <doi:10.1002/jae.1082>.
Under this prior expected value, each country's system follows
a global VAR with country-invariant parameters. Further flexibility is
provided by the hierarchical prior structure that retains the Minnesota prior
interpretation for the global VAR and features estimated prior covariance
matrices, shrinkage, and persistence levels. Bayesian forecasting is developed
for models including exogenous variables, allowing conditional forecasts given
the future trajectories of some variables and restricted forecasts assuring
that rates are forecasted to stay positive and less than 100. The package
implements the model specification, estimation, and forecasting routines,
facilitating coherent workflows and reproducibility. It also includes automated
pseudo-out-of-sample forecasting and computation of forecasting performance
measures. Beautiful plots,
informative summary functions, and extensive documentation complement all
this. An extraordinary computational speed is achieved thanks to employing
frontier econometric and numerical techniques and algorithms written in 'C++'.
The 'bpvars' package is aligned regarding objects, workflows, and code
structure with the 'R' packages 'bsvars' by
Woźniak (2024) <doi:10.32614/CRAN.package.bsvars> and 'bsvarSIGNs' by
Wang & Woźniak (2025) <doi:10.32614/CRAN.package.bsvarSIGNs>, and they
constitute an integrated toolset. Copyright: 2025 International Labour
Organization.

Tomasz Woźniak

bpvars

Forecasting with Bayesian Panel Vector Autoregressions

Miguel Sanchez-Martinez

International Labour Organization 

ilo_dynamic_panel function

A list of 189 <code>ts</code> objects with time series of 34 observations 
on 4 variables:<dl>
 <dt>gdp</dt>
<dd>logarithm of gross domestic product</dd> <dt>UR</dt>
<dd>annual unemployment rate</dd> <dt>EPR</dt>
<dd>annual employment rate</dd> <dt>LFPR</dt>
<dd>annual labour force participation rate</dd>
</dl>

Format

A 4-variable annual system for forecasting labour market outcomes 
for 189 countries from 1991 to 2024 — ilo_dynamic_panel

A list of 189 <code>ts</code> objects with time series of 34 observations 
on 4 variables:<dl>
 <dt>gdp</dt>
<dd>logarithm of gross domestic product</dd>

 <dt>UR</dt>
<dd>annual unemployment rate</dd>

 <dt>EPR</dt>
<dd>annual employment rate</dd>

 <dt>LFPR</dt>
<dd>annual labour force participation rate</dd>


</dl>

ilo_dynamic_panel: A 4-variable annual system for forecasting labour market outcomes for 189 countries from 1991 to 2024

Description

Usage

Arguments

Format

Examples